Biology subjects

Hovig, E.

Publications and source records attributed to Hovig, E..

At least 19 recordsLinked to original sources

bioRxiv2026

Genomic impact of the second plague pandemic on three human populations

The second plague pandemic (early 14th-early 19th centuries), which was caused by Yersinia pestis, had a profound demographic, socio-economic and cultural impact across Eurasia and North Africa. Many regions in Europe and the Middle East are estimated to have lost 40-60% of their human populations, with some areas suffering even higher mortality. Whether exposure to Y. pestis drove strong positive selection on protective genetic variants in the human genome, and how it shaped migration patterns, remains debated, despite several recent studies based on ancient DNA. Here, we analyse a markedly larger, higher coverage, and geographically diverse dataset based on shotgun sequencing of genomes from 529 ancient individuals to a mean depth 8.8x dating to either before or after the arrival of the pandemic at three sites in northern Europe: Trondheim (Norway), Lund (Sweden) and Vilnius (Lithuania). Genome-wide scans for signatures of selection provide no evidence for strong positive selection acting on specific genetic variants driven by Y. pestis exposure: we neither replicate selection signatures reported by previous studies nor identify new genome-wide significant candidates. However, for all three sites, we observe evidence for a reduction in long-range immigration, indicated by a drop in the diversity of ancestry that followed the arrival of Y. pestis and broadly coincided with the end of the Viking Age, Christianisation and the onset of the Little Ice Age. Our results shed important light on the demographic impact of major sociohistorical changes that occurred during the late Medieval period in Scandinavia and the Baltic region and link Christianisation to increased diversity in ancestry before the pandemic.

genomics↗

bioRxiv2026

CANCAN: high-resolution copy number and mutation heterogeneity analysis of DNA sequence data for clinical applications

High-throughput DNA sequencing is central to precision oncology, yet robust and interpretable methods for integrated analysis of copy number alterations and somatic variants across sequencing platforms remain limited. We present CANCAN (Copy number integrative ANalysis in CANcer), a platform-agnostic computational framework for high-resolution analysis of allele-specific copy number and variant data. CANCAN integrates novel normalization and segmentation strategies and enables inference of tumor purity, ploidy, subclonality and mutation multiplicity, while providing statistical confidence estimates and transparent evaluation of alternative solutions. Benchmarking across whole-genome, whole-exome and targeted sequencing datasets from TCGA and the IMPRESS-Norway study demonstrates high concordance with established methods, with particularly strong performance on targeted sequencing data. CANCAN accurately estimates global genomic features, including purity and ploidy, even at reduced sequencing coverage, and shows comparable or improved agreement relative to existing tools. In addition, it provides detailed visualization of the genomic context of clinically relevant biomarkers, supporting diagnostic interpretation. CANCAN constitutes a reproducible and interpretable approach for integrated genomic analysis, addressing key methodological and practical challenges in clinical cancer genomics.

bioinformatics↗

bioRxiv2024

MirGeneDB 3.0: Improved taxonomic sampling, uniform nomenclature of novel conserved microRNA families, and updated covariance models.

We present a major update of MirGeneDB (3.0), the manually curated animal microRNA gene database. Beyond moving to a new server and the creation of a computational mirror, we have expanded the database with the addition of 33 invertebrate species, including representatives of 5 previously unsampled phyla, and 6 mammal species. MirGeneDB now contains entries for 21, 822 microRNA genes (5, 160 of these from the new species) belonging to 1743 microRNA families. The inclusion of these new species allowed us to refine both the evolutionary node of appearance of a number of microRNA genes/families, as well as MirGeneDBs phylogenetically informed nomenclature system. Updated covariance models of all microRNA families, along with all smallRNA read data are now downloadable. These enhanced annotations will allow researchers to analyze microRNA properties such as secondary structure and features of their biogenesis within a robust phylogenetic context and without the database plagued with numerous false positives and false negatives. In light of these improvements, MirGeneDB 3.0 will assume the responsibility for naming conserved novel metazoan microRNAs. MirGeneDB is part of RNAcentral and Elixir Norway, and is publicly and freely available at master.cloud.mirgenedb.org. Key PointsO_LIMajor update to the manually curated and uniformly named microRNA gene database MirGeneDB C_LIO_LI114 animal species, >1700 microRNA families and [~]20 000 genes searchable, browsable and downloadable C_LIO_LINew features to download all smallRNA read data and updated covariance models for each family C_LI

evolutionary biology↗

bioRxiv2024

StarSignDNA: Signature tracing for accurate representation of mutational processes

All cell lineages accumulate mutations over time, increasing the probability that some lineages eventually become malignant. Many of the processes responsible for generating mutations are leaving a characteristic footprint in the genome that allows their presence to be detected. However, the mutational pattern in a tumour is usually the combined result of multiple mutational processes being at work simultaneously, and the problem of disentangling the different footprints and their relative impact then becomes a deconvolution problem. Several algorithms have been developed for this purpose, most of them involving a factorization of the mutation count matrix into two non-negative matrices, representing respectively the underlying mutational signatures and the relative weighting of (or exposure to) these signatures. Here, we introduce the StarSignDNA algorithm for mutational signature analysis, which offers efficient re-fitting and de novo mutational signature extraction. StarSignDNA is capable of deciphering well-differentiated signatures linked to known mutagenic mechanisms and suggesting clinically relevant predictions for a single patient. The package offers a command line-based interface and data visualization routines. Author summaryStarSignDNA is a novel algorithm for identifying mutational signatures from cancer sequencing data. It excels in low mutation count scenarios, balancing signature detection and true signature discovery. StarSignDNA improves prediction accuracy and biological validity by addressing challenges such as overfitting and underfitting, achieving optimal variable selection and shrinkage, and ensuring interpretability. In re-fitting, it reduces prediction variance and handles sparsity, resulting in more precise predictions. In de novo extraction, it improves the detection of challenging signatures and achieves better alignment between detected and known signatures through unsupervised cross-validation. Its unique features, including prediction confidence and customizable reference signatures, offer valuable insights for clinical single-sample analysis.

bioinformatics↗

bioRxiv2022

Finemap-MiXeR: A variational Bayesian approach for genetic finemapping

Discoveries from genome-wide association studies often contain large clusters of highly correlated genetic variants, which makes them hard to interpret. In such cases, finemapping the underlying causal variants become important. Here we present a new method, the Finemap-MiXeR, based on a variational Bayesian approach for finemapping genomic data, i.e., determining the causal single nucleotide polymorphisms (SNPs) associated with a trait at a given locus after controlling for correlation among genetic variants due to linkage disequilibrium. Our approach is based on the optimization of Evidence Lower Bound of the likelihood function obtained from the MiXeR model. The optimization is done using Adaptive Moment Estimation Algorithm, allowing to obtain posterior probability of each SNP to be a causal variant. We tested Finemap-MiXeR in a range of different scenarios, using both synthetic and real data from the UK Biobank, using standing height phenotype as an example. In comparison to the existing finemapping methods FINEMAP and SuSiE methods, we observed that Finemap-MiXeR in most cases has better accuracy. Furthermore, it is computationally efficient, and unlike other methods the complexity is not increasing as the number of causal SNPs or the heritability increases. We show that our finemapping algorithm identifies a small number of genetic variants per locus which are informative for predicting the phenotype in an independent sample.

bioinformatics↗

bioRxiv2022

The Norwegian Mother, Father, and Child cohort study (MoBa) genotyping data resource: MoBaPsychGen pipeline v.1

BackgroundThe Norwegian Mother, Father, and Child Cohort Study (MoBa) is a population-based pregnancy cohort, which includes approximately 114,500 children, 95,200 mothers, and 75,200 fathers. Genotyping of MoBa has been conducted through multiple research projects, spanning several years; using varying selection criteria, genotyping arrays, and genotyping centres. MoBa contains numerous interrelated families, which necessitated the implementation of a family-based quality control (QC) pipeline that verifies and accounts for diverse types of relatedness. MethodsThe MoBaPsychGen pipeline, comprising pre-imputation QC, phasing, imputation, and post-imputation QC, was developed based on current best-practice protocols and implemented to account for the complex structure of the MoBa genotype data. The pipeline includes QC on both single nucleotide polymorphism (SNP) and individual level. Phasing and imputation were performed using the publicly available Haplotype Reference Consortium release 1.1 panel as a reference. Information from the Medical Birth Registry of Norway and MoBa questionnaires were used to identify biological sex, year of birth, reported parent-offspring (PO) relationships, and multiple births (only available in the offspring generation). ResultsIn total, 207,569 unique individuals (90% of the unique individuals included in the study) and 6,981,748 autosomal SNPs passed the MoBaPsychGen pipeline. A further 174,462 chromosome X and 3,200 PAR SNPs are available in a subset of these individuals (N = 204,913 and 135,593, respectively). The relatedness checks performed throughout the pipeline allowed identification of within-generation and across-generation first-degree, second-degree, and third-degree relatives. The individuals passing post-imputation QC comprised 64,471 families ranging in size from singletons to 84 unique individuals (singletons are included as families as other family members may not have been genotyped, imputed, or passed post-imputation QC). The relationships identified include 287 monozygotic twin pairs, 22,884 full siblings, 117,004 PO pairs, 23,299 second-degree relative pairs, and 10,828 third-degree relative pairs. DiscussionMoBa contains a highly complex relatedness structure, with a variety of family structures including singletons, PO duos, full (mother, father, child) PO trios, nuclear families, blended families, and extended families. The availability of robustly quality-controlled genetic data for such a large cohort with a unique extended family structure will allow many novel research questions to be addressed. Furthermore, the MoBaPsychGen pipeline has potential utility in similar cohorts.

genetics↗

bioRxiv2021

immuneML: an ecosystem for machine learning analysis of adaptive immune receptor repertoires

Adaptive immune receptor repertoires (AIRR) are key targets for biomedical research as they record past and ongoing adaptive immune responses. The capacity of machine learning (ML) to identify complex discriminative sequence patterns renders it an ideal approach for AIRR-based diagnostic and therapeutic discovery. To date, widespread adoption of AIRR ML has been inhibited by a lack of reproducibility, transparency, and interoperability. immuneML (immuneml.uio.no) addresses these concerns by implementing each step of the AIRR ML process in an extensible, open-source software ecosystem that is based on fully specified and shareable workflows. To facilitate widespread user adoption, immuneML is available as a command-line tool and through an intuitive Galaxy web interface, and extensive documentation of workflows is provided. We demonstrate the broad applicability of immuneML by (i) reproducing a large-scale study on immune state prediction, (ii) developing, integrating, and applying a novel method for antigen specificity prediction, and (iii) showcasing streamlined interpretability-focused benchmarking of AIRR ML.

bioinformatics↗

bioRxiv2020

Towards a consensus microRNA signature of primary and metastatic colorectal cancer

Although microRNAs (miRNA) are involved in all hallmarks of cancer, miRNA dysregulation in metastasis remains poorly understood and contradictory results have been published. The aim of this work was to identify miRNAs associated with metastatic progression of colorectal cancer (CRC). Novel and previously published next generation sequencing (NGS) datasets generated from 268 samples with primary (pCRC) and metastatic CRC (mCRC; liver, lung and peritoneal metastases) and tumor adjacent tissues were analyzed. Differential expression analysis was performed using a meticulous bioinformatics pipeline, including only bona fide miRNAs, utilizing miRNA-tailored quality control and processing, and applying a physiologically meaningful cut-off value (100 reads per million). The results were adjusted for host tissue background expression and samples from the different metastatic sites were independently analyzed. A metastatic signature containing five miRNAs up-regulated at multiple metastatic sites was identified (Mir-210_3p, Mir-191_5p, Mir-8-P1b_3p (mir-141-3p), Mir-1307_5p, and Mir-155_5p) along with a number of miRNAs that were differentially expressed at individual metastatic sites. Several of these have previously been implicated in metastasis through involvement in epithelial-to-mesenchymal transition and hypoxia, while other identified miRNAs represent novel findings. The identified differentially expressed miRNAs confirm known associations and contribute novel insights into miRNA involvement in the metastatic process. The use of open science practices facilitates reproducibility, and new datasets may easily be added to the publicly available pipeline to continuously improve the knowledge in the field. The identified set of miRNAs provides a reliable starting-point for further research into the role of miRNAs in metastatic progression.

cancer biology↗

bioRxiv2020

Accuracy and efficiency of germline variant calling pipelines for human genome data

Advances in next-generation sequencing technology has enabled whole genome sequencing (WGS) to be widely used for identification of causal variants in a spectrum of genetic-related disorders, and provided new insight into how genetic polymorphisms affect disease phenotypes. The development of different bioinformatics pipelines has continuously improved the variant analysis of WGS data, however there is a necessity for a systematic performance comparison of these pipelines to provide guidance on the application of WGS-based scientific and clinical genomics. In this study, we evaluated the performance of three variant calling pipelines (GATK, DRAGEN and DeepVariant) using Genome in a Bottle Consortium, "synthetic-diploid" and simulated WGS datasets. DRAGEN and DeepVariant show a better accuracy in SNPs and indels calling, with no significant differences in their F1-score. DRAGEN platform offers accuracy, flexibility and a highly-efficient running speed, and therefore superior advantage in the analysis of WGS data on a large scale. The combination of DRAGEN and DeepVariant also provides a good balance of accuracy and efficiency as an alternative solution for germline variant detection in further applications. Our results facilitate the standardization of benchmarking analysis of bioinformatics pipelines for reliable variant detection, which is critical in genetics-based medical research and clinical application.

bioinformatics↗

bioRxiv2020

The genetic structure of Norway

The aim of the present study was to describe the genetic structure of the Norwegian population using genotypes from 6369 unrelated individuals with detailed information about places of residence. Using standard single marker- and haplotype-based approaches, we report evidence of two regions with distinctive patterns of genetic variation, one in the far northeast, and another in the south of Norway, as indicated by fixation indices, haplotype sharing, homozygosity and effective population size. We detect and quantify a component of Uralic Sami ancestry that is enriched in the North. On a finer scale, we find that rates of migration have been affected by topography like mountain ridges. In the broader Scandinavian context, we detect elevated relatedness between the mid- and northern border areas towards Sweden. The main finding of this study is that despite Norways long maritime history and as a former Danish territory, the region closest to mainland Europe in the south appears to have been the most isolated region in Norway, highlighting the open sea as a barrier to gene flow.

genetics↗

bioRxiv2019

Cancer Predisposition Sequencing Reporter (CPSR): a flexible variant report engine for germline screening in cancer

The value of high-throughput germline genetic testing is increasingly recognized in clinical cancer care. Disease-associated germline variants in cancer patients are important for risk management and surveillance, surgical decisions, and can also have major implications for treatment strategies since many are in DNA repair genes. With the increasing availability of high-throughput DNA sequencing in cancer clinics and research, there is thus a need to provide clinically oriented sequencing reports for germline variants and their potential therapeutic relevance on a per-patient basis. To meet this need we have developed the Cancer Predisposition Sequencing Reporter (CPSR), an open-source computational workflow that generates a structured report of germline variants identified in known cancer predisposition genes, highlighting markers of therapeutic, prognostic, and diagnostic relevance. A fully automated variant classification procedure based on more than 30 refined ACMG criteria represents an integral part of the workflow. Importantly, the set of cancer predisposition genes profiled in the report can be flexibly chosen from more than 40 virtual gene panels established by scientific experts, enabling customization of the report for different screening purposes and clinical contexts. The report can be configured to also list actionable secondary variant findings as recommended by ACMG, as well as the status of low-risk variants from genome-wide association studies in cancer. CPSR demonstrates superior sensitivity and comparable specificity for the detection of pathogenic variants when compared to existing algorithms. Technically, the tool is implemented in Python/R, and is freely available through Docker technology. Source code, documentation, example reports, and installation instructions are accessible via the project GitHub page: https://github.com/sigven/cpsr.

bioinformatics↗

bioRxiv2019

Paternally expressed imprinted Snord116 and Peg3 regulate hypothalamic orexin neurons

Imprinted genes are highly expressed in the hypothalamus; however, whether specific imprinted genes affect hypothalamic neuromodulators and their functions is unknown. It has been suggested that Prader-Willi syndrome (PWS), a neurodevelopmental disorder caused by lack of paternal expression at chromosome 15q11-q13, is characterised by hypothalamic insufficiency. Here, we investigate the role of the paternally expressed Snord116 gene within the context of sleep and metabolic abnormalities of PWS, and we report a novel role of this imprinted gene in the function and organisation of the two main neuromodulatory systems of the lateral hypothalamus (LH), namely, the orexin (OX) and melanin concentrating hormone (MCH) systems. We observe that the dynamics between neuronal discharge in the LH and the sleep-wake states of mice with paternal deletion of Snord116 (PWScrm+/p-) are compromised. This abnormal state-dependent neuronal activity is paralleled by a significant reduction in OX neurons in the LH of mutants. Therefore, we propose that an imbalance between OX- and MCH-expressing neurons in the LH of mutants reflects a series of deficits manifested in the PWS, such as dysregulation of rapid eye movement (REM) sleep, food intake and temperature control.\n\nHighlightsO_LISnord116 regulates neuronal activity in the lateral hypothalamus (LH), which is time-locked with cortical states of sleep.\nC_LIO_LILoss of Snord116 reduces orexin neurons in the LH and affects sleep homeostasis and thermoregulation in mice.\nC_LIO_LISnord116 and Peg3 independently control orexin expression in the LH.\nC_LIO_LIPaternally expressed alleles maximize the patrilineal effects in the control of REM sleep by the LH in mammals.\nC_LI

neuroscience↗

bioRxiv2019

Tankyrase inhibition sensitizes melanoma to PD-1 immune checkpoint blockade in syngeneic mouse models

The development of immune checkpoint inhibitors represents a major breakthrough in cancer therapy. Nevertheless, a substantial number of patients fail to respond to checkpoint pathway blockade. {beta}-catenin is the key transcriptional regulator of WNT/{beta}-catenin signaling. Evidence for {beta}-catenin-mediated immune evasion is found in 13% of all cancers, 42% of primary cutaneous melanoma and a mouse melanoma model. Currently, there are no therapeutic strategies available for targeting WNT/{beta}-catenin signaling to counteract checkpoint inhibitor resistance in melanoma. Here we report that a specific small-molecule tankyrase inhibitor, G007-LK, attenuates WNT/{beta}-catenin and YAP signaling pathways in the syngeneic murine B16-F10 melanoma model enabling sensitivity to anti-PD-1 immune checkpoint therapy. RNA sequencing of 18 tankyrase inhibitor-treated human melanoma cell lines and B16-F10 cells revealed a transcriptional response profile for a subpopulation. This cell line sub-group displayed elevated baseline YAP signaling activity and was susceptible to reduce melanocyte inducing transcription factor (MITF) expression upon tankyrase inhibition.

cancer biology↗

bioRxiv2018

Rationally designed mimotope library for profiling of the human IgM repertoire

Specific antibody reactivities are routinely used as biomarkers but the use of antibody repertoire profiles is still awaiting recognition. Here we suggest to expedite the adoption of this class of system level biomarkers by rationally designing a peptide array as an efficient probe for an appropriately chosen repertoire compartment. Most IgM antibodies are characterized by few somatic mutations, polyspecificity and physiological autoreactivity with housekeeping function. Previously, probing this repertoire with a set of immunodominant self-proteins provided only coarse information on repertoire profiles. In contrast, here we describe the rational selection of a peptide mimotope set, appropriately sized as a potential diagnostic, that also represents optimally the diversity of the human public IgM reactivities. A 7-mer random peptide phage display library was panned on pooled human IgM. Next generation sequencing of the selected phage yielded a non-exhaustive set of 224087 mimotopes which clustered in 790 sequence clusters. A set of 594 mimotopes, representative of the most significant clusters, was used to demonstrate that this approach samples symmetrically the space of IgM reactivities. When probed with diverse patients sera in an oriented peptide array, this set produced a higher and more dynamic signal as compared to 1) random peptides, 2) random peptides purged of mimotope-like sequences and 3) mimotopes from a small subset of clusters. In this respect, the representative library is an optimized probe of the human IgM diversity. Proof of principle predictors for randomly selected diagnoses based on the optimized library demonstrated that it contains more than 1070 different profiles with the capacity to correlate with diverse pathologies. Thus, an optimized small library of IgM mimotopes is found to address very efficiently the dynamic diversity of the human IgM repertoire providing informationally dense and structurally interpretable IgM reactivity profiles. Author SummaryThe presence in the blood of antibodies specific for a particular infectious agent is used routinely as a diagnostic tool. The overall profile of available antibody reactivities (or their repertoire) in an individual has been studied much less. As an omics approach to immunity it can be a rich source of information about the system beyond just the individual history of antigenic exposure. Using a subset of antibodies - IgM, which are involved also in housekeeping functions like removing dead cells, and bacteriophage based techniques for selection of specific peptides, we managed to define a non-exhaustive set of 224087 peptides recognized by IgM antibodies present in most individuals. They were found to group naturally in 790 structural groups. Limiting these to the most outstanding 594 groups, we used one representative from each group to assemble a reasonably small set of peptides that extracts the maximum information from the antibody repertoire at a minimum cost per test. We demonstrate, that this representative peptide library is a better probe of the human IgM diversity than comparably sized libraries constructed on other principles. The optimized library contains more than 1070 different potentially profiles useful for the diagnosis, prognosis or monitoring of inflammatory and infectious conditions, tumors, neurodegenerative diseases, etc.

immunology↗

bioRxiv2018

MirGeneDB2.0: the curated microRNA Gene Database

Small non-coding RNAs have gained substantial attention due to their roles in animal development and human disorders. Among them, microRNAs are unique because individual gene sequences are conserved across the animal kingdom. In addition, unique and mechanistically well understood features can clearly distinguish bona fide miRNAs from the myriad other small RNAs generated by cells. However, making this separation is not a common practice and, thus, not surprisingly, the heterogeneous quality of available miRNA complements has become a major concern in microRNA research. We addressed this by extensively expanding our curated microRNA gene database MirGeneDB to 45 organisms that represent the full taxonomic breadth of Metazoa. By consistently annotating and naming more than 10,900 microRNA genes in these organisms, we show that previous microRNA annotations contained not only many false positives, but surprisingly lacked more than 2,100 bona fide microRNAs. Indeed, curated microRNA complements of closely related organisms are very similar and can be used to reconstruct Metazoan evolution. MirGeneDB represents a robust platform for microRNA-based research, providing deeper and more significant insights into the biology and evolution of miRNAs but also biomedical and biomarker research. MirGeneDB is publicly and freely available at http://mirgenedb.org/.

genomics↗

bioRxiv2017

Sample-Index Misassignment Impacts Tumor Exome Sequencing

Sample pooling enabled by dedicated indexes is a common and cost-effective strategy used in high-throughput DNA sequencing. Index misassignment leading to cross-sample contamination has however been described as a general problem of sequencing instruments which utilize exclusion amplification. Using real-life data from multiple tumor sequencing projects, we demonstrate that co-multiplexed samples can induce artifactual calls closely resembling high-quality somatic variant calls, and argue that dual indexing is the most reliable countermeasure.

genomics↗

bioRxiv2017

Personal Cancer Genome Reporter: Variant Interpretation Report For Precision Oncology

SummaryIndividual tumor genomes pose a major challenge for clinical interpretation due to their unique sets of acquired mutations. There is a general scarcity of tools that can i) systematically interrogate cancer genomes in the context of diagnostic, prognostic, and therapeutic biomarkers, ii) prioritize and highlight the most important findings, and iii) present the results in a format accessible to clinical experts. We have developed a stand-alone, open-source software package for somatic variant annotation that integrates a comprehensive set of knowledge resources related to tumor biology and therapeutic biomarkers, both at the gene and variant level. Our application generates a tiered report that will aid the interpretation of individual cancer genomes in a clinical setting.\n\nAvailability and ImplementationThe software is implemented in Python/R, and is freely available through Docker technology. Documentation, example reports, and installation instructions are accessible via the project GitHub page: https://github.com/sigven/pcgr)\n\nContactsigven@ifi.uio.no

bioinformatics↗

bioRxiv2017

Genome Build Information Is An Essential Part Of Genomic Track Files

Genomic locations are represented as coordinates on a specific genome build version, but the build information is frequently missing when coordinates are provided. It is essential to correctly interpret and analyse the genomic intervals contained in genomic track files. Here, we demonstrate that this crucial metadatum (or rather datum) is often isolated from the genomic track files in public repositories and journal articles, which could be a major time thief. We propose best practices to ensure that genome build version is always carried along with genomic track files. Although not a substitute to the best practices, we also provide a tool to predict the genome build version of genomic track files.

bioinformatics↗